Abstract
Independent cart conveyor system is an emerging technology in industries, trying to replace servo motors and kinematic chains in several applications. It consists of several carts on a closed-loop path, each of which can freely move with respect to the other carts. Basically, each cart is an servo linear motor, where the windings and the drives are on the frame and the magnets are on the moving carts together with a feedback device (e.g. a Hall sensor to track the position). The drive controls and actuates each cart independently according to the motion profile loaded. From a mechanical point of view, the carts are connected to the frame through a series of rollers placed on and under a mechanical guide. The rollers may be subject to a premature wear and the condition monitoring of these components is a no trivial challenge, due to non-stationary working conditions of variable speed profile and variable loads. This paper provides a bearing fault model taking into account the motion profile of the cart, the mechanical design of the cart, the geometry of the conveyor path, the expected loads and the type of fault on the roller bearings.
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References
Rockwell Automation: iTRAK The Intelligent Track System Increase machine flexibility and throughput to enhance overall productivity. http://literature.rockwellautomation.com/idc/groups/literature/documents/br/motion-br007_-en-p.pdf
Beckhoff Automation: XTS. The eXtended Transport System. https://download.beckhoff.com/download/Document/Catalog/XTS_Beckhoff_e.pdf
B&R: ACOPOStrak Ultimate Production Effectiveness. https://www.br-automation.com/smc/5adafdb3a7f954f17c8bb25652a8c971a38e4d94.pdf
Molano JCC, Rossi S, Cocconcelli M, Rubini R (2017) Dynamic model of an independent carts system. In: Advances in Italian mechanism science. Mechanisms and machine science, vol 47, pp 379–387
Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126
Poyhonen S, Jover P, Hyotyniemi H (2004) Signal processing of vibrations for condition monitoring of an induction motor. In: Proceedings of 1st international symposium control, communications signal processing, pp 499–502
Nandi S, Toliyat H, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans Energy Convers 20(4):719–729
Randall RB (2011) Vibration-based condition monitoring: industrial, aerospace and automotive application. Wiley, Hoboken
Curcurú G, Cocconcelli M, Immovilli F, Rubini R (2001) On the detection of distributed roughness on ball bearings via stator current energy: experimental results. Diagnostyka 51(3):17–21
Immovilli F, Cocconcelli M, Bellini A, Rubini R (2009) Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Trans Ind Electron 56(11):4710–4717
Hayes M (1996) Statistical Digital Signal Processing and Modeling. Wiley, Hoboken
Trajin B, Chabert M, Regnier J, Faucher J (2009) Hilbert versus Concordia transform for three-phase machine stator current time- frequency monitoring. Mech Syst Signal Process 23(8):2648–2657
Salami M, Gani A, Pervez T (2001) Machine condition monitoring and fault diagnosis using spectral analysis techniques. In: Proceedings of the 1st international conference on mechatronics, pp 690–700
Wang W, McFadden PD (1993) Early detection of gear failure by vibration analysis I. Calculation of the time-frequency distribution. Mech. Syst. Signal Process. 7(3):193–203
Wang W, McFadden PD (1996) Application of wavelets to gearbox vibration signals for fault detection. J Sound Vib 192(5):927–939
Cerrada M, Snchez R-V, Li C, Pacheco F, Cabrera D, Valente de Oliveira J, Vsquez RE (2018) A review on data-driven fault severity assessment in rolling bearings. Mech Syst Signal Process 99:169–196
McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high frequency resonance technique a review. Tribol Int 117:3–10
McFadden PD, Smith JD (1984) Model for the vibration produced by a single point defect. J Sound Vib 96:69–82
McFadden PD, Smith JD (1984) The vibration produced by multiple point defects in a rolling element bearing. J Sound Vib 98:263–273
Su YT, Lin SJ (1992) On initial detection of a tapered roller bearing frequency domain analysis. J Sound Vib 155:75–84
Ho D, Randall RB (2000) Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech Syst Signal Process 14:763–788
D’Elia G, Cocconcelli M, Mucchi E (2018) An algorithm for the simulation of faulted bearings in non-stationary conditions. Meccanica 53(45):1147–1166
Acknowledgments
The authors are grateful for the National University Research Fund (FAR 2016) of the University of Modena and Reggio Emilia - Departmental and Interdisciplinary Projects (DR. 73/2017, Prot. n. 37510-27/02/2017) and the support from Tetra Pak Packaging Solutions.
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Cocconcelli, M., Cavalaglio Camargo Molano, J., Rubini, R., Capelli, L., Borghi, D. (2019). Bearing Fault Model for an Independent Cart Conveyor. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_22
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DOI: https://doi.org/10.1007/978-3-030-11220-2_22
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